{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GPyOpt: configuring Scikit-learn methods\n",
    "\n",
    "### Written by Javier Gonzalez and Zhenwen Dai, University of Sheffield.\n",
    "\n",
    "Modified by Federico Tomasi, University of Genoa.\n",
    "\n",
    "*Last updated Thursday, 28 September 2017.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part I: Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this notebook is to use GPyOpt to tune the parameters of Machine Learning algorithms. In particular, in this section we will show how to tune the hyper-parameters for the [Support Vector Regression (SVR)](http://www.svms.org/regression/SmSc98.pdf) implemented in [Scikit-learn](http://scikit-learn.org/stable/). Given the standard interface of Scikit-learn, other models can be tuned in a similar fashion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start loading the requires modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline  \n",
    "import GPy\n",
    "import GPyOpt\n",
    "import numpy as np\n",
    "from sklearn import svm\n",
    "from numpy.random import seed\n",
    "seed(12345)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example we will use the Olympic marathon dataset available in GPy. \n",
    "We split the original dataset into the training data (first 20 data points) and testing data (last 7 data points). The performance of SVR is evaluated in terms of Rooted Mean Squared Error (RMSE) on the testing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's load the dataset\n",
    "GPy.util.datasets.authorize_download = lambda x: True # prevents requesting authorization for download.\n",
    "data = GPy.util.datasets.olympic_marathon_men()\n",
    "X = data['X']\n",
    "Y = data['Y']\n",
    "X_train = X[:20]\n",
    "Y_train = Y[:20,0]\n",
    "X_test = X[20:]\n",
    "Y_test = Y[20:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first see the results with the default kernel parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "svr = svm.SVR()\n",
    "svr.fit(X_train,Y_train)\n",
    "Y_train_pred = svr.predict(X_train)\n",
    "Y_test_pred = svr.predict(X_test)\n",
    "print(\"The default parameters obtained: C=\"+str(svr.C)+\", epilson=\"+str(svr.epsilon)+\", gamma=\"+str(svr.gamma))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We compute the RMSE on the testing data and plot the prediction. With the default parameters, SVR does not give an OK fit to the training data but completely miss out the testing data well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE = 0.56330740612\n"
     ]
    },
    {
     "data": {
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LF4KZNMk/lxXYi7Yr9UXw+X05TPsoi5EjoU4FvdZFVRFJBVUe525mI8xsRPjtAGCNma0C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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20fe2243d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(X_train,Y_train_pred,'b',label='pred-train')\n",
    "plot(X_test,Y_test_pred,'g',label='pred-test')\n",
    "plot(X_train,Y_train,'rx',label='ground truth')\n",
    "plot(X_test,Y_test,'rx')\n",
    "legend(loc='best')\n",
    "print(\"RMSE = \"+str(np.sqrt(np.square(Y_test_pred-Y_test).mean())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try Bayesian Optimization. We first write a wrap function for fitting with SVR. The objective is the RMSE from cross-validation. We optimize the parameters in *log* space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nfold = 3\n",
    "def fit_svr_val(x):\n",
    "    x = np.atleast_2d(np.exp(x))\n",
    "    fs = np.zeros((x.shape[0],1))\n",
    "    for i in range(x.shape[0]):\n",
    "        fs[i] = 0\n",
    "        for n in range(nfold):\n",
    "            idx = np.array(range(X_train.shape[0]))\n",
    "            idx_valid = np.logical_and(idx>=X_train.shape[0]/nfold*n, idx<X_train.shape[0]/nfold*(n+1))\n",
    "            idx_train = np.logical_not(idx_valid)\n",
    "            svr = svm.SVR(C=x[i,0], epsilon=x[i,1],gamma=x[i,2])\n",
    "            svr.fit(X_train[idx_train],Y_train[idx_train])\n",
    "            fs[i] += np.sqrt(np.square(svr.predict(X_train[idx_valid])-Y_train[idx_valid]).mean())\n",
    "        fs[i] *= 1./nfold\n",
    "    return fs\n",
    "\n",
    "## -- Note that similar wrapper functions can be used to tune other Scikit-learn methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We set the search interval of $C$ to be roughly $[0,1000]$ and the search interval of $\\epsilon$ and $\\gamma$ to be roughtly $[1\\times 10^{-5},0.1]$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "domain       =[{'name': 'C',      'type': 'continuous', 'domain': (0.,7.)},\n",
    "               {'name': 'epsilon','type': 'continuous', 'domain': (-12.,-2.)},\n",
    "               {'name': 'gamma',  'type': 'continuous', 'domain': (-12.,-2.)}]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We, then, create the GPyOpt object and run the optimization procedure. It might take a while."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = GPyOpt.methods.BayesianOptimization(f = fit_svr_val,            # function to optimize       \n",
    "                                          domain = domain,         # box-constraints of the problem\n",
    "                                          acquisition_type ='LCB',       # LCB acquisition\n",
    "                                          acquisition_weight = 0.1)   # Exploration exploitation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SDA2FHxGRBtldCdOYMJHUS28SVqoSNmdO+ElSCQNVw0SkoXL5gfk6O1Ik9dKb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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20fae6b990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# it may take a few seconds\n",
    "opt.run_optimization(max_iter=50)\n",
    "opt.plot_convergence()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's show the best parameters found. They differ significantly from the default parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best parameters obtained: C=430.280588436, epilson=0.00104925901026, gamma=6.14421235333e-06\n"
     ]
    }
   ],
   "source": [
    "x_best = np.exp(opt.X[np.argmin(opt.Y)])\n",
    "print(\"The best parameters obtained: C=\"+str(x_best[0])+\", epilson=\"+str(x_best[1])+\", gamma=\"+str(x_best[2]))\n",
    "svr = svm.SVR(C=x_best[0], epsilon=x_best[1],gamma=x_best[2])\n",
    "svr.fit(X_train,Y_train)\n",
    "Y_train_pred = svr.predict(X_train)\n",
    "Y_test_pred = svr.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see SVR does a reasonable fit to the data. The result could be further improved by increasing the *max_iter*. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE = 0.0709714822355\n"
     ]
    },
    {
     "data": {
      "image/png": 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smv5RiJW/ncxRR8E55/jJzL7/Pt6RSqrQaSCRWAhPFWC5uXTJyaHLNyHcw4P4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\nLiISQEruIiIBpOQuIhJA/x/1Z3k6mc6cCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20f4625f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(X_train,Y_train_pred,'b',label='pred-train')\n",
    "plot(X_test,Y_test_pred,'g',label='pred-test')\n",
    "plot(X_train,Y_train,'rx',label='ground truth')\n",
    "plot(X_test,Y_test,'rx')\n",
    "legend(loc='best')\n",
    "print(\"RMSE = \"+str(np.sqrt(np.square(Y_test_pred-Y_test).mean())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part II: Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the same way as we did for the regression task, we can tune the parameters for a classification problem. The function to optimise is treated as a black box, so the procedure is very similar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can start by loading the standard Iris dataset from scikit-learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qCAZ8IqKCYMAnIioIBnwiooL4f41oaNBlAU08AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20f43fa090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris, make_blobs\n",
    "\n",
    "# iris = load_iris()\n",
    "\n",
    "# # Take the first two features. We could avoid this by using a two-dim dataset\n",
    "# X = iris.data[:, :2]\n",
    "# y = iris.target\n",
    "\n",
    "X, y = make_blobs(centers=3, cluster_std=4, random_state=1234)\n",
    "\n",
    "for i in np.unique(y):\n",
    "    idx = y == i\n",
    "    plt.plot(X[idx,0], X[idx,1], 'o')\n",
    "# plt.title(\"First two dimensions of Iris data\")\n",
    "# plt.xlabel('Sepal length')\n",
    "# plt.ylabel('Sepal width');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, let's divide the dataset into a \"learning\" and a \"test\" set. The test set will be used later, in order to assess the performance of parameters selected by GPyOpt in relation to [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) and a SVC with the default parameters.\n",
    "\n",
    "For each case, we consider an SVM with RBF kernel.\n",
    "\n",
    "Let's plot both the learning (with colors related to their classes) and the test data (red \"x\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QSH7Nm7f1JjNdXcHxkijCznIiChSSX5Mn99yRrLJj2eTJ2bYrQSqxLUWgZLbk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\n0P8HJF+bQxPAQ9AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20f4420750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "train_index, test_index = next(StratifiedShuffleSplit(test_size=.4, random_state=123).split(X,y))\n",
    "\n",
    "X_train, X_test = X[train_index], X[test_index]\n",
    "y_train, y_test = y[train_index], y[test_index]\n",
    "\n",
    "for i in np.unique(y_train):\n",
    "    idx = y_train == i\n",
    "    plt.plot(X_train[idx,0], X_train[idx,1], 'o')\n",
    "\n",
    "plt.plot(X_test[:,0], X_test[:,1], 'rx');\n",
    "plt.title(\"Learning and test sets\")\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel('Sepal width');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 SVC with no parameter search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first tentative is to use the SVC with the default parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC with default parameters\n",
      "Test score: 0.700\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc = SVC(kernel='rbf').fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = svc.predict(X_train)\n",
    "y_test_pred = svc.predict(X_test)\n",
    "\n",
    "print(\"SVC with default parameters\\nTest score: %.3f\" % svc.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# utility functions taken from\n",
    "# http://scikit-learn.org/stable/auto_examples/svm/plot_iris.html\n",
    "\n",
    "def make_meshgrid(x, y, h=.02):\n",
    "    \"\"\"Create a mesh of points to plot in\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x: data to base x-axis meshgrid on\n",
    "    y: data to base y-axis meshgrid on\n",
    "    h: stepsize for meshgrid, optional\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    xx, yy : ndarray\n",
    "    \"\"\"\n",
    "    x_min, x_max = x.min() - 1, x.max() + 1\n",
    "    y_min, y_max = y.min() - 1, y.max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                         np.arange(y_min, y_max, h))\n",
    "    return xx, yy\n",
    "\n",
    "\n",
    "def plot_contours(ax, clf, xx, yy, **params):\n",
    "    \"\"\"Plot the decision boundaries for a classifier.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ax: matplotlib axes object\n",
    "    clf: a classifier\n",
    "    xx: meshgrid ndarray\n",
    "    yy: meshgrid ndarray\n",
    "    params: dictionary of params to pass to contourf, optional\n",
    "    \"\"\"\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    out = ax.contourf(xx, yy, Z, **params)\n",
    "    return out\n",
    "\n",
    "X0, X1 = X[:, 0], X[:, 1]\n",
    "xx, yy = make_meshgrid(X0, X1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualise the surfaces defined by the SVM to differentiate the classes. The points have the color of each own class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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NG6DiDk5RKh2wgoYeSdrGfMINEorZziBYNfJTSjfI0qw6KIBDtHGDLM2az+qq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\nyQKUMcaYRLIAZYwxJpH+Hw41KiYg5rEtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20f4387990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1,1, squeeze=False)\n",
    "    \n",
    "for i in np.unique(y_test):\n",
    "    plot_contours(ax[0,0], svc, xx, yy, alpha=0.8)\n",
    "    idx = y_test == i\n",
    "    ax[0,0].plot(X_test[idx,0], X_test[idx,1], 'o')\n",
    "    ax[0,0].set_title(\"Difference between SVM prediction (background color) \\nand real classes (point color) in test set\")\n",
    "#     ax[0,0].set_xlim([4,8])\n",
    "#     ax[0,0].set_ylim([2,4.5])\n",
    "    \n",
    "# plt.xlabel('Sepal length')\n",
    "# plt.ylabel('Sepal width');\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For comparison, we can use the standard [GridSearchCV](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html) for the parameter selection. This will build a grid with the combination of all of the parameters, and then select the best combination of parameters that achieve the maximum validation score (or minimum error)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV\n",
      "Test score: 0.850\n",
      "Best parameters extracted: {'C': 0.21544346900318834, 'gamma': 0.01}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "grid = GridSearchCV(SVC(kernel='rbf'), dict(C=np.logspace(-2,4,10), gamma=np.logspace(-12,-2,10))).fit(X_train,y_train)\n",
    "\n",
    "\n",
    "print(\"GridSearchCV\\nTest score: %.3f\" % grid.score(X_test, y_test))\n",
    "\n",
    "print(\"Best parameters extracted: %s\" % grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gvam3Aul0CjwFtqX3a+/M4rd2rYDXCLTS6RR4WkAnFr+183ABGoFW2sm0+U/V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\nERHJlAKPiIhk6v8DeMf8NdrXbFEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20f4058fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# y_test_pred = grid.predict(X_test)\n",
    "\n",
    "f, ax = plt.subplots(1,1, squeeze=False)\n",
    "    \n",
    "for i in np.unique(y_test):\n",
    "    plot_contours(ax[0,0], grid, xx, yy, alpha=0.8)\n",
    "    idx = y_test == i\n",
    "    ax[0,0].plot(X_test[idx,0], X_test[idx,1], 'o')\n",
    "    ax[0,0].set_title(\"Difference between SVM prediction (background color) \\n\"\n",
    "                      \"and real classes (point color) in test set, using GridSearchCV\")\n",
    "#     ax[0,0].set_xlim([4,8])\n",
    "#     ax[0,0].set_ylim([2,4.5])\n",
    "    \n",
    "# plt.xlabel('Sepal length')\n",
    "# plt.ylabel('Sepal width');\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 GPy optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "domain       =[{'name': 'C',      'type': 'continuous', 'domain': (-2.,4.)},\n",
    "#                {'name': 'kernel', 'type': 'categorical', 'domain': (0, 1)},\n",
    "               {'name': 'gamma',  'type': 'continuous', 'domain': (-12.,-2.)}\n",
    "              ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "    \n",
    "def fit_svc_val(x, mdl=None, cv=None):\n",
    "    x = np.atleast_2d(np.exp(x))\n",
    "    fs = np.zeros((x.shape[0], 1))\n",
    "    for i, params in enumerate(x):\n",
    "        dict_params = dict(zip([el['name'] for el in domain], params))\n",
    "        if 'kernel' in dict_params:\n",
    "            dict_params['kernel'] = 'rbf' if dict_params['kernel'] == 0 else 'poly'\n",
    "        mdl.set_params(**dict_params)\n",
    "        fs[i] = -np.mean(cross_val_score(mdl, X_train, y_train, cv=cv))\n",
    "    return fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The set cost function is ignored! LBC acquisition does not make sense with cost.\n"
     ]
    },
    {
     "data": {
      "image/png": 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jzlbzbWrqf0ZjNosWhcsDNTTsfJkgEZGEHWcjbtPQDyK1rraCrTT9tSCUESF7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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20a4609990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from functools import partial\n",
    "opt = GPyOpt.methods.BayesianOptimization(f = partial(fit_svc_val, mdl=SVC(kernel='rbf')),  # function to optimize       \n",
    "                                          domain = domain,         # box-constrains of the problem\n",
    "                                          acquisition_type ='LCB',       # LCB acquisition\n",
    "                                          acquisition_weight = 0.2)   # Exploration exploitation\n",
    "opt.run_optimization(max_iter=50)\n",
    "opt.plot_convergence()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7ReTOtLs/SbP/GYZhzCtMDBqGYRiGsehQ1dd30eYTwCeazt0IyKDGZRjG/EBVKOv8NwUW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\nOEBMBg3DMAzDMAzDMA6Q/x8IJ+1S1kTOnAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20a45ddd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "opt.plot_acquisition()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPyOpt\n",
      "Test score: 0.850\n",
      "Best parameters extracted: {'C': 2.03757065667337, 'gamma': 0.0033852993544374603}\n"
     ]
    }
   ],
   "source": [
    "x_best = np.exp(opt.X[np.argmin(opt.Y)])\n",
    "best_params = dict(zip([el['name'] for el in domain], x_best))\n",
    "\n",
    "svc_opt = SVC(**best_params)\n",
    "svc_opt.fit(X_train, y_train)\n",
    "# y_train_pred = svc.predict(X_train)\n",
    "# y_test_pred = svc.predict(X_test)\n",
    "\n",
    "\n",
    "print(\"GPyOpt\\nTest score: %.3f\" % svc_opt.score(X_test, y_test))\n",
    "\n",
    "print(\"Best parameters extracted: %s\" % best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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P3pOTRinWObVacILGNTP/MvBvwIlm9qSZvQ34JHC+mf0SOC9+7mgahl5qpffk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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20a4416ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# y_test_pred = grid.predict(X_test)\n",
    "\n",
    "f, ax = plt.subplots(1,1, squeeze=False)\n",
    "    \n",
    "for i in np.unique(y_test):\n",
    "    plot_contours(ax[0,0], svc_opt, xx, yy, alpha=0.8)\n",
    "    idx = y_test == i\n",
    "    ax[0,0].plot(X_test[idx,0], X_test[idx,1], 'o')\n",
    "    ax[0,0].set_title(\"Difference between SVM prediction (background color) \\n\"\n",
    "                      \"and real classes (point color) in test set, using GPyOpt\")\n",
    "#     ax[0,0].set_xlim([4,8])\n",
    "#     ax[0,0].set_ylim([2,4.5])\n",
    "    \n",
    "# plt.xlabel('Sepal length')\n",
    "# plt.ylabel('Sepal width');\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameter selected by GridSearchCV and GPyOpt are very similar. However, it is clear from the convergence plots that GPyOpt converges very quickly to the minimum, while GridSearch has to test each combination of parameters."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
